Put count_classes funtction into the WCv1LMDBReader class to get it standalone

WCv1LMDBReader.py

@@ -8,7 +8,6 @@ import numpy as np
 from enum import Enum
 import safetensors.torch
 from torch.utils.data import Dataset
-from flumapping.utils.Utils import one_hot_encode, count_classes
 
 """
 tensors = {
@@ -179,6 +178,52 @@ class WCv1LMDBReader(Dataset):
             self.keys()
             return status
 
+    def count_classes(self, map: np.ndarray | torch.Tensor,output_type=None) -> np.ndarray | torch.Tensor:
+        """function to count the class proportions of an segmentation map.
+    
+        Args:
+            map (np.ndarray | torch.Tensor): input map on which the class proportions needs to be counted. Input can be a single map (np.ndarray) or batched tensor of multiple maps (torch.Tensor)
+    
+        Returns:
+            np.ndarray | torch.Tensor: the class proportions of the input map. 
+        """
+        if len(map.shape) == 3:
+            map = map.squeeze()
+        
+        if type(map) == np.ndarray:
+            map = torch.tensor(map, dtype=torch.float32)
+        
+        output = []
+        num_pixel = map.shape[0] * map.shape[1]
+        
+        for i in range(10, 110, 10):
+            if len(map.shape) == 4:
+                percentage = torch.sum(torch.where(map == i, 1, 0), dim=(1,2,3)) / num_pixel
+            else:
+                percentage = torch.sum(torch.where(map == i, 1, 0)) / num_pixel
+            output.append(percentage)
+            
+            if i == 90:
+                if len(map.shape) == 4:
+                    percentage = torch.sum(torch.where(map == i, 1, 0), dim=(1,2,3)) / num_pixel
+                else:
+                    percentage = torch.sum(torch.where(map == i, 1, 0)) / num_pixel
+                output.append(percentage)
+        
+        if len(map.shape) == 4:
+            class_props = torch.stack(output, dim=1)
+            class_props.requires_grad = True
+        else:
+            class_props = torch.tensor(output)
+        
+        if type(map) == np.ndarray:
+            return class_props.cpu().detach().numpy()
+        if type(map) == torch.Tensor:
+            if output_type is not None:
+                return class_props.type(dtype=output_type)
+            else:
+                return class_props
+
     def __len__(self):
         if self._keys is None:
             self.logger.info("keys are not loaded yet")
@@ -254,5 +299,5 @@ class WCv1LMDBReader(Dataset):
             if "classprops" in tensor_dict.keys():
                 class_props = tensor_dict['classprops']
             else:
-                class_props = count_classes(tensor_dict['wcmap'])
+                class_props = self.count_classes(tensor_dict['wcmap'])
             return (image, label, class_props)